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At least 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

$\bar{\nu}_\mu$ charged-current $\pi^0$ data release

Data release for the NOvA muon antineutrino charged-current (CC) pi^0 cross section presented in arXiv:2511.05807. The signal for this analysis is defined as muon antineutrino CC interactions in the fiducial volume of the NOvA near detector (a 2.7 m × 2.7 m × 9.0 m region) that produce at least one pi^0 in the final state emerging from the nucleus, within the phase space of muon momentum [0.5, 2.5) GeV/c and muon angle [0, 60) degree, as described in arXiv:2511.05807. The released zip file contains two files: NOvA_NumubarCCPi0_DataRelease.root README.txt The ROOT file includes the cross-section results as well as the statistical and systematic covariance matrices for each variable used in this analysis. The README provides a detailed description of the contents of the data release. Official Flux: The flux used in this analysis is available from the NOvA Public Docs: https://publicdocs.fnal.gov/cgi-bin/ShowDocument?docid=8. File structure --- The ROOT file contains the following TDirectories: pi0p - pi^0 momentum distributions pi0dir - pi^0 angular distributions muonp - muon momentum distributions muondir - muon angular distributions Q2 - reconstructed Q^2 distributions Wmass - reconstructed W_mass distributions Each directory contains three histograms: xsec (TH1D): Cross section result cov_stat (TH2D): Statistical covariance matrix cov_syst (TH2D): Systematic covariance matrix Usage notes: - xsec gives the measured differential cross section w.r.t. the corresponding variable. - cov_stat and cov_syst provide the full covariance matrices. - The bin definitions and kinematic phase spaces follow those used in arXiv:2511.05807. Citation --- If you use these data, please cite: NOvA Collaboration, arXiv:2511.05807.

Wu, Wanwei [Pittsburgh U.] (ORCID:0000000326327215↗
DOE OSTI2026

Two datasets are better than one: method of double moments for 3D reconstruction in cryo-EM

Cryo-electron microscopy is a powerful imaging technique for reconstructing three-dimensional molecular structures from noisy tomographic projection images of randomly oriented particles. We introduce a new data fusion framework, termed the method of double moments, which reconstructs molecular structures from two instances of the second-order moment of projection images obtained under distinct orientation distributions: one uniform, the other non-uniform and unknown. We prove that these moments generically uniquely determine the underlying structure, up to a global rotation and reflection, and we develop a convex-relaxation-based algorithm that achieves accurate recovery using only second-order statistics. Our results demonstrate the advantage of collecting and modeling multiple datasets under different experimental conditions, illustrating that leveraging dataset diversity can substantially enhance reconstruction quality in computational imaging tasks.

Kam’s method↗
DOE OSTI2018

Physical property characteristics of gas hydrate-bearing reservoir and associated seal sediments collected during NGHP-02 in the Krishna-Godavari Basin, in the offshore of India

India’s National Gas Hydrate Program Expedition 02 (NGHP-02) was conducted to better understand geologic controls on gas hydrate occurrence and morphology, targeting coarse-grained sediments along the lower continental slope offshore eastern India. This study combines seismic, logging-while-drilling data, and a petroleum system approach to provide a regional geologic and lithologic context for: 1) gas hydrate morphology and distribution, and 2) effects of fine-grained sediments (clays and other grains smaller than 63µm) on gas production in NGHP-02 Area B in the Krishna-Godavari Basin. Area B seismic data show a buried anticline/syncline structure with strong reflectors, R1 and R2, that delineate two of the five lithologic units: Unit I (shallowest), II (R1), III, IV (R2) and V (below the bas of gas hydrate stability). Throughout Area B, gas hydrate morphology depends on its placement within these units. Specifically, core- and grain-scale measurements indicate fines content exerts a primary control on the gas hydrate distribution and morphology. Units I, II and III are generally fine-grained. On the anticline crest, these units host primarily grain-displacing gas hydrate veins linked to pore-occupying gas hydrate in thin, localized, coarser-grained deposits. Diatoms in Unit III increase porosity with depth, reaching ~70% where it contacts Unit IV, the gas hydrate reservoir associated with reflector R2. The Unit III lithology and porosity allow fluid and dissolved-phase methane to escape Unit IV and form gas hydrate in the fine-grained overburden. Within Unit IV, fine-grained layers are interbedded with coarser-grained gas hydrate reservoir sands, and the fines content even in the sands is high enough to impact hydraulic and mechanical properties during gas production. Fluid motion during gas production can mobilize fines, which can then clog pore throats, limiting production rates. In conclusion, pore-water freshening during gas hydrate dissociation can increase fines mobilization, particularly given the smectite identified in the fine-grained interbeds.

03 NATURAL GAS↗
DOE OSTI2017

Sublocalization of Cytochrome b 6 f Complexes in Photosynthetic Membranes

It is well established that the majority of energy-converting photosynthetic protein complexes in plant thylakoid membrane are nonhomogenously distributed between stacked and unstacked membrane regions. Yet, the sublocalization of the central cytochrome b 6 f complex remains controversial. We present a structural model that explains the variation in cytochrome b 6 f sublocalization data. Small changes in the distance between adjacent membranes in stacked grana regions either allow or restrict access of cytochrome b 6 f complexes to grana. Furthermore, if the width of the gap falls below a certain threshold, then the steric hindrance prevents cytochrome b 6 f access to grana. Evidence is presented that the width of stromal gap is variable, demonstrating that the postulated mechanism can regulate the lateral distribution of the cytochrome b 6 f complexes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Adaptive methods of generating complex light arrays

Structured light arrays of various shapes have been a cornerstone in optical science, driven by the complexities of precise and adaptable generation. This study introduces an approach using a spatial light modulator (SLM) as a generator for these arrays. By projecting a holographic mask onto the SLM, it functions simultaneously as an optical convolution device, focusing mechanism, and structured light beam mask. Our approach offers unmatched versatility, allowing for the experimental fabrication of traditional beam arrays like azimuthal Laguerre–Gaussian (LG), Bessel–Gaussian (BG), and Hermite–Gauss (HG) in the far-field. Notably, it has enabled a method of generating Ince–Gauss (IG) and LG radial mode beam arrays using a convolution solution. Our system provides exceptional control over array periodicity and intensity distribution, bypassing the Talbot self-imaging phenomenon seen in traditional setups. We provide an in-depth theoretical discussion, supported by empirical evidence, of our far-field results. This method has vast potential for applications in optical communication, data processing, and multi-particle manipulation. It paves the way for rapid generation of structured light with high spatial frequencies and complex shapes, promising transformative advances in these domains.

Optics↗
DOE OSTI2024

Investigating the influence of particle distribution on force and torque statistics using hierarchical machine learning

An accurate representation of hydrodynamic force and torque experienced by every particle in a distribution can be obtained from particle resolved (PR) simulations. These unique quantities are influenced by the deterministic position of surrounding particles. However, systems simulated with this methodology are typically limited to particles due to the involved computational cost. This resource requirement is a major bottleneck in analyzing the effect of variations in particle distribution. Here, this article attempts to address this bottleneck by availing relatively inexpensive deep learning models. The surrogate models that we employ in this article use a physics‐based hierarchical framework and symmetry‐preserving neural networks to achieve robustness with limited training data. This article first performs additional generalizability tests on PR data of distinct distributions that are not involved in the training process. The models are then deployed on several different particle distributions. Impact of clustering and structure on the observed statistics are investigated.

42 ENGINEERING↗
DOE OSTI2020

Stochastic Simulation of Daily Suspended Sediment Concentration Using Multivariate Copulas

Estimation of daily suspended sediment concentration (SSC) is required for water resources and environment management. In this paper, a copula-based stochastic method was proposed for daily SSC simulation. Here, the multivariate copula function, constructed based on a bivariate copula and two bivariate conditional probability distributions, was used to model the temporal and cross dependence structures in daily SSCs. Then, the daily SSCs were generated by sampling from the multivariate conditional distribution. As a result, synthetic long-term SSCs data beyond the limited observation period can be provided for water resources managers, which plays a critical role in accurately estimating frequency and magnitude of extreme SSCs events. The proposed method was under rigorous examination by applying to a case study at Pingshan station in the Jinsha River Basin, China. Results showed that the generated daily SSC sequences not only had a high degree of accuracy in preserving the statistical characteristics of the daily SSC observations, but also captured both the temporal correlation and the cross-correlation between the daily streamflow and daily SSC. Specifically, the average daily relative error values corresponding to mean, standard deviation, skewness, lag-1 temporal correlation, and cross correlation were 0.87%, 4.24%, 7.52%, 0.51% and 2.02%, respectively. The multivariate copula framework proposed here can accurately and efficiently generate long-term daily SSC data for water resources management such as frequency analysis and risk assessment of extreme SSC events.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Neutron diffraction discriminates between models for the nanoarchitecture of graphene sheets in glassy carbon

Glassy carbon is a chemically inert, biocompatible, disordered material with the graphene sheet as its basic building block. Structural characterisation techniques have so far been unable to provide definitive distinction between the many proposed models for its structure. Computer based simulation methods have made a step forward by predicting structure from interaction potentials, but the results are sensitive to the choice of the potential. Here we use the white neutron beam of the Spallation Neutron Source at Oak Ridge National Laboratory coupled with the SNAP time-of-flight diffractometer to sample the reciprocal space of glassy carbon and calculate accurate radial distribution functions. From the radial distribution information, we determine graphene sheet dimensions, registration, curvature, and assess defect content to rank the proposed structures in terms of their agreement with experimental data. In this work, we find that the recent models derived from empirical potentials give the best agreement.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Formation and transformation of iron oxy-hydroxide precursor clusters to ferrihydrite

Iron (Fe) oxy-hydroxide minerals such as ferrihydrite (Fh) are ubiquitous in Earth-surface environments and important in biogeochemical element cycling. Recent research has suggested that their formation is preceded by the precipitation of ultrasmall (~1 nm) Keggin-like Fe oxy-hydroxide clusters. However, relatively little is understood about the structure of the precursor clusters and the impacts of pH and time on their growth and transformation to more stable phases. Here, we used a new method that involves mixed flow reactors (MFR) to synthesize these Fe oxy-hydroxide precursor clusters at pH 1.0, 1.5, 2.5, and 4.5. In situ and ex situ synchrotron scattering measurements and laboratory small-angle X-ray scattering (SAXS) were used to study the structure and size of Fe oxy-hydroxide clusters and their transformation products, respectively. Results show that with increasing pH, the particle size and structural order of samples increase, forming solids that resemble 2-line Fh at pH 4.5. The experimental data were compared with X-ray pair distribution functions (PDF) calculated for a range of Fe(III) oxyhydroxide clusters, including Fe 13 Keggin isomers computed previously using density functional theory (DFT), which yielded at best only partial agreement at short range (<5 Å). Aging of the clusters synthesized at pH 1.5 and 2.5 results in growth and transformation via Ostwald ripening to mixtures of goethite (Gt) and lepidocrocite (Lp). This process was inhibited by immediately reacting the early-formed clusters with phosphate (PO 4 3– ), suggesting that oxyanion surface complexes can stabilize the initial clusters by preventing growth and crystallization to more stable phases.

58 GEOSCIENCES↗
DOE OSTI2016

FT-IR and Zeta potential measurements on TiO nanoparticles

In the present investigation, ultrafine TiO particles have been synthesized successfully by thermal decomposition method. The sample was characterized by X-ray diffraction (XRD) and Fourier transform infrared (FTIR) spectroscopy. As-synthesized TiO nanoparticles have a cubic structure as characterized by power X-ray diffraction (XRD), which shows that TiO nanoparticles have narrow size distribution with particle size 11.5 nm. FTIR data shows a strong peak at 1300 cm{sup −1}, assignable to the Ti-O stretching vibrations mode.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2016

Counter-ion binding and mobility in the presence of hydrophobic polyions – combining molecular dynamics simulations and NMR

Counter-ion binding and mobility in aqueous solutions of partially hydrophobic ionene oligoions is studied here by a combination of all-atomic molecular dynamics (MD) simulations and NMR ({sup 19}F and {sup 81}Br nuclei) measurements. We present results for 12, 12–ionenes in the presence of different halide ions (F{sup −}, Cl{sup −}, Br{sup −} and I{sup −}), as well as their mixtures; the latter allowing us to probe counter-ion selectivity of these oligoions. We consolidate both structural and dynamic information, in particular simulated radial distribution functions and average residence times of counter-ions in the vicinity of ionenes and NMR data in the form of counter-ion chemical shift and self-diffusion coefficients. On one hand, previously reported enthalpy of dilution and mixing measurements show a reverse counter-ion sequence for 12, 12–ionenes with respect to their less hydrophobic 3, 3– and 6, 6– analogues. On the other hand, the current MD and NMR data, reflecting the counter-ion binding tendencies to the ionene chain, give evidence for the same ordering as that observed by MD for 3, 3–ionenes. This is not seen as a contradiction and can be rationalized on the basis of increasing chain hydrophobicity, which has different consequences for enthalpy and ion-binding. The latter is reflecting free energy changes and as such includes both enthalpic and entropic contributions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Machine Learning for Mapping Multipactor Susceptibility in RF Systems: Capabilities and Generalization Constraints

Multipactor is a surface-driven electron avalanche phenomenon that degrades the performance and reliability of radio-frequency (RF) systems in particle accelerator and vacuum electronics applications. Multipactor behavior in a given device structure is conventionally assessed through susceptibility charts, which provide a parameter-space characterization of the instability. In this work, we assess the capabilities of machine-learning (ML) models to learn and predict such susceptibility charts and analyze the constraints governing their generalization across materials. Using a simulation-derived dataset spanning six distinct secondary-electron-yield material profiles in a canonical two-surface planar geometry, we train supervised regression models and artificial neural networks to predict the time-averaged electron growth rate, δavg, across the relevant parameter space. Model performance is evaluated using metrics that explicitly probe the structure of susceptibility charts, including Intersection over Union, Structural Similarity Index, and correlation analysis. Tree-based ensemble models outperform neural-network models in reconstructing susceptibility regions and in generalizing across material domains. Principal-component analysis reveals disjoint material feature distributions, indicating that the piecewise mode structure of multipactor susceptibility is difficult to represent with a single global model and that generalization is constrained by data coverage rather than by model complexity. An exhaustive reduced-coverage study further shows that sparse material-space coverage can yield mean performance in the same general range but producing large variability in the susceptibility-region overlap. These results clarify the capabilities of ML-based surrogate models for parameter-space characterization of multipactor discharge. They also provide guidance for their appropriate use in RF system design.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Atomic structure of an FeCrMoCBY metallic glass revealed by high energy x-ray diffraction

Amorphous bulk metallic glasses with the composition Fe 48 Cr 15 Mo 14 C 15 B 6 Y 2 have been of interest due to their special mechanical and electronic properties, including corrosion resistance, high yield-strength, large elasticity, catalytic performance, and soft ferromagnetism. Here, we apply a reverse Monte Carlo technique to unravel the atomic structure of these glasses. The pair-distribution functions for various atomic pairs are computed based on the high-energy x-ray diffraction data we have taken from an amorphous sample. Monte Carlo cycles are used to move the atomic positions until the model reproduces the experimental pair-distribution function. The resulting fitted model is consistent with our ab initio simulations of the metallic glass. Our study contributes to the understanding of functional properties of Fe-based bulk metallic glasses driven by disorder effects.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Unveiling the internal structure of the Hercules supercluster

ABSTRACT We have investigated the structure of the Hercules supercluster (SCL160) based on data originally extracted from the Sloan Digital Sky Survey (SDSS-DR7). We have traced the mass distribution in the field through the numerical density, weighted by the r′ luminosity of the galaxies, and classified objects based on their spatial position and redshift. This has allowed us to address not only the kinematics of the supercluster as a whole, but also the internal kinematics of each cluster, which had not been explored further before. We have confirmed that the Hercules supercluster is composed of the galaxy clusters A2147, A2151, and A2152. A2151 consists of five subclusters, A2147 two, and A2152 at least two. They form the heart of the Hercules supercluster. We also have found two other gravitationally bound clusters, therefore increasing the known members of the supercluster. We have estimated a total mass of 2.1 ± 0.2 × 1015 M⊙ for the Hercules supercluster. To determine the dynamical masses in this work, we have resorted to the M200−σ scaling relation and the caustic technique. Comparing both methods with simulated data of bimodal merging clusters, we found that the caustic, as well as the σ-based masses, is biased through the merger age, showing a boost just after pericentric passage. This is not in line with the principle of the caustic method, which affirms it does not depend on the cluster dynamical state.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Spin structure of the proton from global QCD analysis

In this talk we review recent results for spin-dependent parton distribution functions extracted in global QCD analysis of high energy scattering data by the JAM collaboration, including inclusive and semi-inclusive deep-inelastic scattering, jet and weak boson production in polarised hadron-hadron collisions. In particular, we focus on the determination of the gluon polarisation in the proton, whose sign and magnitude have been the subject of debate recently.

Melnitchouk, Wally [Thomas Jefferson National Acce↗
DOE OSTI2016

Accurate optimization of amino acid form factors for computing small-angle X-ray scattering intensity of atomistic protein structures

Structure modelling via small-angle X-ray scattering (SAXS) data generally requires intensive computations of scattering intensity from any given biomolecular structure, where the accurate evaluation of SAXS profiles using coarse-grained (CG) methods is vital to improve computational efficiency. To date, most CG SAXS computing methods have been based on a single-bead-per-residue approximation but have neglected structural correlations between amino acids. To improve the accuracy of scattering calculations, accurate CG form factors of amino acids are now derived using a rigorous optimization strategy, termed electron-density matching (EDM), to best fit electron-density distributions of protein structures. This EDM method is compared with and tested against other CG SAXS computing methods, and the resulting CG SAXS profiles from EDM agree better with all-atom theoretical SAXS data. By including the protein hydration shell represented by explicit CG water molecules and the correction of protein excluded volume, the developed CG form factors also reproduce the selected experimental SAXS profiles with very small deviations. Taken together, these EDM-derived CG form factors present an accurate and efficient computational approach for SAXS computing, especially when higher molecular details (represented by the q range of the SAXS data) become necessary for effective structure modelling.

Tong, Dudu↗
DOE OSTI2023

Systems and methods for generating a graded lattice structure and their application to additive manufacturing

Systems and methods for generating graded lattice structures that can be used as infill for additively manufactured articles. Tailored sectioning and field-based smoothing are modified polygon, e.g., circle, packing algorithms that adjust the size of the circles based on physical field data to adapt the infill generation process to a field expected to be experienced by the article. Molecular dynamically generated lattice infill is based on force balancing a node distribution instead of a circle packing. Field data can be utilized to adjust the spacing of the node distribution according to a force balance equilibrium model that accounts for the field expected to be experienced by the article being additively manufactured. The resultant non-uniform honeycomb structures from tailored sectioning, field-based smoothing, and force-balancing robustly and efficiently address the connection issues with traditional non-uniform lattice structures.

Kim, Seokpum↗
DOE OSTI2017

Impulsively Generated Wave Trains in Coronal Structures. I. Effects of Transverse Structuring on Sausage Waves in Pressureless Tubes

The behavior of the axial group speeds of trapped sausage modes plays an important role in determining impulsively generated wave trains, which have often been invoked to account for quasi-periodic signals with quasi-periods of the order of seconds in a considerable number of coronal structures. We conduct a comprehensive eigenmode analysis, both analytically and numerically, on the dispersive properties of sausage modes in pressureless tubes with three families of continuous radial density profiles. We find a rich variety of the dependence on the axial wavenumber k of the axial group speed v{sub gr}. Depending on the density contrast and profile steepness as well as on the detailed profile description, the v{sub gr}--k curves either possess or do not possess cutoff wavenumbers, and they can behave in either a monotonical or non-monotonical manner. With time-dependent simulations, we further show that this rich variety of the group speed characteristics heavily influences the temporal evolution and Morlet spectra of impulsively generated wave trains. In particular, the Morlet spectra can look substantially different from the “crazy tadpoles” found for the much-studied discontinuous density profiles. We conclude that it is necessary to re-examine available high-cadence data to look for the rich set of temporal and spectral features that can be employed to discriminate between the unknown forms of the density distributions transverse to coronal structures.

79 ASTRONOMY AND ASTROPHYSICS↗